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EPSRC Funds NQCC Studentships Via £6M Industrial Landscape Awards

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June 10, 2026 by Ivy Delaney

The EPSRC will distribute funding via Industrial Doctoral Landscape Awards to support six new doctoral studentships across the UK, focused on advancing quantum computing research. These studentships, offered through the National Quantum Computing Centre’s (NQCC) scheme, aim to bridge the gap between academic research and industrial application, fostering a new generation of skilled graduates for the growing quantum computing sector. The NQCC envisions the UK harnessing the power of quantum computing to solve complex and challenging problems, and this scheme is a key step towards realizing that goal. Universities interested in participating must submit Expressions of Interest (EOI) by July 14th, initiating a competitive process with final results expected in early November.

EPSRC Industrial Doctoral Landscape Awards Scheme Details

The National Quantum Computing Centre (NQCC) is currently supporting six fully-funded doctoral studentships through the EPSRC Industrial Doctoral Landscape Awards (IDLA) scheme, a commitment designed to accelerate the development of scalable quantum computing technologies within the United Kingdom. These studentships, the third cohort of the NQCC’s Doctoral Studentship Scheme, represent a focused effort to bridge the divide between academic research and practical industrial application, specifically targeting challenges in quantum hardware, software, and applications. Universities intending to host these studentships face a swift turnaround; the deadline for submitting Expressions of Interest (EOI) is July 14th, beginning a two-stage application process. Successful EOI submissions will be invited to develop full proposals in collaboration with an NQCC co-supervisor, with shortlisted applicants informed of the outcome in early August.

The NQCC is prioritizing research aligned with its technology program, which addresses key scaling challenges, and particularly welcomes proposals within five technical teams: trapped ions, superconducting circuits, tweezer arrays, software and control systems, and quantum applications. Specific research themes within these areas, such as ion trap chip fabrication and noise-aware compilation, are highlighted as areas of particular interest. The scheme’s aims extend beyond pure research, explicitly seeking to generate deeply skilled academic graduates who are highly employable across the UK quantum computing value chain. Applicants are encouraged to focus on research with a clear path to scaling or application, rather than fundamental investigations, reflecting the NQCC’s industrial focus. Interested parties can submit their EOI response form, and further guidance is available via Dr. Oindrila Deb at nqcc-studentships@stfc.ac.uk, ensuring prospective students and institutions have the resources needed to participate in this competitive program.

NQCC Priority Research Themes for Quantum Computing

The United Kingdom is actively bolstering its quantum computing capabilities through a new wave of doctoral studentships, building on existing efforts to translate theoretical advances into scalable technologies. Each team has defined priority research themes designed to accelerate progress in specific areas. Prospective applicants face a compressed timeline; the deadline for submitting EOIs is July 14th, with shortlisted candidates invited to develop full proposals by September 20th. Interested parties should contact Dr. Oindrila Deb at nqcc-studentships@stfc.ac.uk for further guidance and to access the EOI response form.

To help achieve this vision, the NQCC’s technology programme addresses key challenges associated with scaling quantum computing.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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